CortexOS

An observability layer for AI agent memory.

CortexOS sits between a memory system and the LLM that reads from it. For each retrieval it records which memories were returned, scores how much each one influenced the response, tracks what the retrieved context cost in tokens, and keeps a provenance graph so a stored memory can be traced back to the interaction that created it. It is designed to wrap existing memory systems rather than replace them: the SDK ships as a wrapper around any Mem0-compatible client.

Live site: cortexadev.com

Repository layout

This is a monorepo with three independent subprojects.

Directory	Stack	What it is
cortex-engine	Python 3.11+, FastAPI, SQLAlchemy, Postgres	The backend API and attribution engine
cortex-dashboard	Next.js 16, React 19, TypeScript, Tailwind CSS 4	The operator dashboard UI
cortex-os	Vite 6, React 18, TypeScript, Tailwind CSS 4	The public marketing and docs site

cortex-engine

The backend service. It is a FastAPI application (cortex.api.app:app) that exposes a REST API under /api/v1 with five routers:

• memories - create, list, fetch and delete memory units.
• transactions - a two-phase retrieval protocol; a search initiates a pending transaction, and reporting the model's response completes it and triggers scoring.
• attribution - attribution scores per transaction and per memory.
• health - per-agent health and contradiction checks.
• dashboard - an aggregated overview endpoint.

A plain /healthz endpoint is served at the root for liveness checks.

Attribution uses EAS (Embedding Attribution Score), implemented in cortex/attribution/eas.py. It scores each retrieved memory by the product of its cosine similarity to the response and its cosine similarity to the query, then normalizes the scores so they sum to 1. The computation is a numpy batch matrix multiply over the retrieved set, so it is O(k*d) in the number of retrieved memories and the embedding dimension. Embeddings come from sentence-transformers using all-MiniLM-L6-v2 at 384 dimensions by default.

Persistence is Postgres via async SQLAlchemy and asyncpg, with three Alembic migrations covering the initial schema, calibration and cost configuration, and the provenance graph. The provenance design is written up in cortex-engine/PROVENANCE.md and cortex-engine/docs/provenance-graph.md.

cortex/sdk/wrapper.py provides CortexMemory, an OpenTelemetry-instrumented wrapper around a Mem0-compatible client. Every backend call in the wrapper is guarded, so the SDK does not crash the host application if the engine is unreachable.

Tests live in cortex-engine/tests and run under pytest with asyncio_mode = "auto".

cortex-dashboard

The Next.js App Router dashboard. It has pages for the overview, agents, memory, memory flow, attribution, cockpit, terminal, health, compliance, analytics, alerts, engine status, settings and docs. Charts and graph views are built with Recharts, D3, React Flow and Framer Motion.

cortex-dashboard/lib/engines holds TypeScript implementations of two attribution algorithms, ContextCite and Shapley value computation, plus a metrics calculator, all covered by Jest tests. Several dashboard views are driven by lib/mock-data.ts rather than live engine data, and the /engine page polls http://localhost:8000/healthz to show whether the Python backend is running. cortex-dashboard/README.md is the longer product write-up and demo walkthrough.

cortex-os

The public site, built with Vite and React. It is a single-page app with client-side routing for /, /docs and /manifesto, and includes the hero, trace demo, provenance graph, cost section, FAQ and waitlist sections. .github/workflows/deploy.yml builds this project on every push to main and publishes cortex-os/dist to GitHub Pages, served at the custom domain in cortex-os/public/CNAME.

Running locally

cortex-engine

Requires Python 3.11 or newer and a running Postgres instance.

cd cortex-engine
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
alembic upgrade head
uvicorn cortex.api.app:app --reload --port 8000

The API is then on http://localhost:8000, with interactive docs at http://localhost:8000/docs.

Settings are read from the environment with the CORTEX_ prefix, so the database connection can be overridden with CORTEX_DATABASE_URL and CORTEX_DATABASE_URL_SYNC. The defaults point at postgresql://postgres:cortex@localhost:5432/cortex. CORS defaults allow http://localhost:3000 and http://localhost:3001.

Run the tests with:

pytest

cortex-dashboard

Requires Node.js 20 or newer.

cd cortex-dashboard
npm install
npm run dev

The dashboard runs on http://localhost:3000. It renders without the backend, but the engine page will report the API as offline until cortex-engine is running on port 8000.

Other scripts: npm run build, npm start, npm run lint, npm test.

cortex-os

cd cortex-os
npm install
npm run dev

Vite serves the site on http://localhost:5173. Use npm run build to type-check and produce dist, and npm run preview to serve that build.